Traditionally, LIDAR odometry relies on point cloud registration to detect feature points, e.g. line and surface segments, and uses a matching algorithm to obtain the pose transformation by minimizing the distance between two consecutive point-cloud scans.
Data-driven methods consider solving LIDAR odometry in an end-to-end fashion, by leveraging deep neural networks to construct a mapping function from point cloud scan sequences to pose estimates [1]}, [2]}, [3]}.
As point cloud data are challenging to be directly ingested by neural networks due to their sparse and irregularly sampled format,
these methods typically convert point clouds into a regular matrix through cylindrical projection, and adopt ConvNets to extract features from consecutive point cloud scans. These networks regress relative poses and are trained via ground-truth labels.
LO-Net [2]} reports competitive performance over the conventional state-of-the-art algorithm, i.e. the LIDAR Odometry and Mapping (LOAM) algorithm [5]}.
